Get access

Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources

Authors

  • Sophie Ancelet,

    Corresponding authorCurrent affiliation:
    1. Institute of Radiological Protection and Nuclear Safety (IRSN), Laboratory of Epidemiology (LEPID), Fontenay-aux-Roses, France
    • AgroParisTech/INRA UMR518, Department of Applied Mathematics and Informatics (MIA), France
    Search for more papers by this author
    • **Work started while Sophie Ancelet was at the Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK.

  • Juan J. Abellan,

    1. Centre for Public Health Research (CSISP), Valencia, Spain
    2. CIBER Epidemiología y Salud Pública (CIBERESP), Spain
    Search for more papers by this author
  • Víctor J. Del Rio Vilas,

    1. Department of Food Environment and Rural Affairs (Defra), London, UK
    2. Veterinary Laboratories Agency, Addlestone, Surrey, UK
    Search for more papers by this author
  • Colin Birch,

    1. Veterinary Laboratories Agency, Addlestone, Surrey, UK
    Search for more papers by this author
  • Sylvia Richardson

    1. Department of Epidemiology and Public Health, Imperial College London, London, UK
    Search for more papers by this author

Corresponding author: e-mail: sophie.ancelet@irsn.fr or sophie.ancelet@orange.fr, Phone: +33-158-357-989, Fax: +33-146-570-386.

Abstract

When analyzing the geographical variations of disease risk, one common problem is data sparseness. In such a setting, we investigate the possibility of using Bayesian shared spatial component models to strengthen inference and correct for any spatially structured sources of bias, when distinct data sources on one or more related diseases are available. Specifically, we apply our models to analyze the spatial variation of risk of two forms of scrapie infection affecting sheep in Wales (UK) using three surveillance sources on each disease. We first model each disease separately from the combined data sources and then extend our approach to jointly analyze diseases and data sources. We assess the predictive performances of several nested joint models through pseudo cross-validatory predictive model checks.

Ancillary